Search Results - Malicious JavaScript*

A Graph Learning Approach for Malicious Javascript Detection

Authors: Ngoc C. Lê Duong Thai Quach Tien-Manh Nguyen et al.

Source: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783032023612

A Combined Model for JavaScript Malicious Code Detection Based on Deep Q-Networks

Authors: Wenming Wang Hongru Song Jiaolong Zheng

Source: Proceedings of the 2025 5th International Conference on Automation Control, Algorithm and Intelligent Bionics. :344-349

A Novel Runtime Monitoring Framework for WebAssembly-Obfuscated Malicious JavaScript Classification

Authors: Bangyan Ju Yu Zhao Chong Yu et al.

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Authors: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa et al.

Source: IEEE Access, Vol 11, Pp 102727-102745 (2023)

Subject Terms: source code representation, cyber security, graph neural network, Abstract syntax tree, Electrical engineering. Electronics. Nuclear engineering, malicious JavaScript detection, TK1-9971

Access URL: https://doaj.org/article/96b921f1abd0426fa01fe22f24802029

Hybrid Detection of Malicious Portable Document Format (PDFs): Safeguarding Against Embedded JavaScript Attacks

Authors: S. S. Iyengar Seyedsina Nabavirazavi Yashas Hariprasad et al.

Source: Signals and Communication Technology ISBN: 9783031893261

Malicious Javascript Detection based on Clustering Techniques

Authors: Nguyen Hong Son Dung, Ha Thanh

Source: International Journal of Network Security & Its Applications. 13:11-21

Subject Terms: Malicious JavaScript, Detection model, Prediction support parameter, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, K-means clustering algorithm

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

Authors: Liang, Zhihong Wang, Xin Hu, Zhenhuang et al.

Subject Terms: Machine Learning, FOS: Computer and information sciences, Cryptography and Security, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2507.22447

Malicious JavaScript Detection in Realistic Environments with SVM and MLP Models

Authors: Ngoc Minh Phung Mamoru Mimura

Source: Journal of Information Processing. 32:748-756

JSMBox—A Runtime Monitoring Framework for Analyzing and Classifying Malicious JavaScript

Authors: Phu H. Phung Allen Varghese Bojue Wang et al.

Source: Communications in Computer and Information Science ISBN: 9783031752001

JSHint: Revealing API Usage to Improve Detection of Malicious JavaScript

Authors: Shaown Sarker Kasimir Schulz Aleksandr Nahapetyan et al.

Source: Lecture Notes in Computer Science ISBN: 9783031757631

An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors

Authors: Kunlun Ren Weizhong Qiang Yueming Wu et al.

Source: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. :1420-1432

A study of malicious JavaScript detection methods in a realistic environment

Authors: Phung Minh Ngoc

File Description: application/pdf

Access URL: https://ndlsearch.ndl.go.jp/books/R100000039-I14460990

Degree: 博士(工学) -- 大学改革支援・学位授与機構

TransAST: A Machine Translation-Based Approach for Obfuscated Malicious JavaScript Detection

Authors: Yan Qin Weiping Wang Zixian Chen et al.

Source: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :327-338

JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

Authors: Kunlun Ren Weizhong Qiang Yueming Wu et al.

Source: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :339-351

Detection Approach of Malicious JavaScript Code Based on deep learning

Authors: Liyuan Zheng Dongcheng Zhang Xin Xie et al.

Source: 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). :1075-1079

Research on Evasion and Detection of Malicious JavaScript Code

Authors: Yujie Ma Haokai Wu Yu-an Tan et al.

Source: Lecture Notes in Computer Science ISBN: 9789819724574

Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement

Authors: Samuel Ndichu Sangwook Kim Seiichi Ozawa

Source: CAAI Transactions on Intelligence Technology (2020)

Subject Terms: obscure code, obfuscated benign js codes, learned feature vectors, 02 engineering and technology, long short-term memory model, QA76.75-76.765, vectors, js code editor, 0202 electrical engineering, electronic engineering, information engineering, Computer software, fasttext model, feature extraction, unpacking, java, term frequency–inverse document frequency model, original js code, invasive software, text analysis, multilayer obfuscation, machine learning models detection, dud-preprocessed obfuscated malicious js codes, formatted js code, paragraph vector models, Computational linguistics. Natural language processing, learning (artificial intelligence), deobfuscation methods, internet, P98-98.5, undetectable code

Access URL: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/trit.2020.0026
https://doaj.org/article/1ec26bbd25b54437bb5faa62e4cf5359

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Authors: Zixian Chen Weiping Wang Yan Qin et al.

Source: Computers & Security. 153:104390

Research on Malicious JavaScript Detection Technology Based on LSTM

Authors: Yong Fang Cheng Huang Liang Liu et al.

Source: IEEE Access, Vol 6, Pp 59118-59125 (2018)

Subject Terms: JavaScript, 0202 electrical engineering, electronic engineering, information engineering, bytecode, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, malicious code detection, LSTM, word vector, TK1-9971

Access URL: https://doaj.org/article/10eaad4b416443e996b2dfbad65517e4

Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection

Authors: Scaria Alex T. Dhiliphan Rajkumar

Source: International Journal of Intelligent Systems. 36:7153-7176

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology